Systems Biology Platform for Integrative Cancer Biology Research

描述（申请人提供）：癌症是分子医学中最复杂、研究最全面的疾病领域。肿瘤研究中积累了大量的小实验数据和组学数据集，目前没有一个生命科学信息学平台能够对这些数据进行全面的处理和有意义的功能分析。在此，我们建议在我们成熟的人体系统生物学平台MetaCore/ metdrug的基础上构建这样一个系统，MetaMiner （Oncology）。该系统将包括一个大型结构化的癌症领域知识数据库，包括基因-疾病关联、抗癌化合物、癌症特异性途径和在GeneGo手工注释超过3年的扰动网络。MetaMiner将主要用于癌症研究中不同类型组学数据的功能分析，多平行测序数据，全基因组甲基化，SNP和基因拷贝数分析，基因表达，蛋白质组学和代谢组学，以及不同类型数据在途径和网络上的交叉相关。这些工具将包括8个功能本体的丰富分析程序，一个用于网络和交互组分析的综合工具包。MetaMiner将与主要的OMICs硬件供应商、第三方生物信息学软件、工作流软件包、转化医学平台、公共领域资源（如caBig）集成。在第一阶段的范围内，我们建议从两个方面进一步推进癌症系统生物学技术。首先，我们将开发一个模块，用于基于功能描述符在大型癌症患者队列中聚集相同类型的组学样本（例如，微阵列表达谱）。这种方法将有助于选择临床不同的患者亚群（簇），具有独特的功能生物标志物和途径相关药物靶点组合。其次，我们将应用我们的算法进行拓扑网络分析，以整合同一患者不同类型组学数据之间的关联。该方法已成功应用于个体化医学和转化医学。在这里，我们建议在现成产品的层次上实现它。最后，我们将开发MetaMiner的集成数据库模式和接口。第一阶段的最终交付成果将是功能原型。公共卫生相关性：我们建议开发一个全面的系统级分析系统，用于癌症研究中的综合数据分析。该平台名为MetaMiner（肿瘤学），将包括一个关于癌症生物学和人类生物学的综合知识数据库，包括蛋白质相互作用、基因-疾病关联、途径和网络，以及与癌症相关的药物化学。MetaMiner将处理任何类型的癌症组学数据，并实现其全面的功能分析。该系统将包括基于功能描述符的癌症患者聚类和基于网络拓扑的不同类型数据集成的新工具。

DESCRIPTION (provided by applicant): Cancer is the most complex and the most comprehensively studied disease area in molecular medicine. A vast ocean of small experiments data and OMICs datasets is being accumulated in oncology research, and none of the currently available life sciences informatics platforms is capable of a comprehensive handling and meaningful functional analysis of these data. Here we propose to build such a system, MetaMiner (Oncology) on the base of our mature human systems biology platform MetaCore/MetaDrug. The system will include a large structured database of cancer domain knowledge, including gene-disease associations, anti-cancer compounds, cancer-specific pathways and perturbed networks manually annotated at GeneGo for over 3 years. MetaMiner will be primarily applied for functional analysis of different types of OMICs data in cancer research multi-parallel sequencing data, genome-wide methylation, SNP and gene copy number assays, gene expression, proteomics and metabolomics, and cross-coreketion of data of different types on pathways and networks. The tools will include an enrichment analysis procedures in 8 functional ontologies, a comprehensive toolkit for network and interactome analyses. MetaMiner will be integrated with the major OMICs hardware vendors, third parties bionformatics software, workflow software packages, translational medicine platforms, public domain resources such as caBig. In the scope of Phase I, we propose to further advance the technology of cancer systems biology in two ways. First, we will develop a module for clustering OMICs samples of the same type (for instance, microaray expression profiles) in large cancer patient cohorts based on functional descriptors. This method will help for selecting clinically distinct patients' sub-populations (clusters) with unique combination of functional biomarkers and pathway-linked drug targets. Second, we will apply our algorithms for topological network analysis for integration of associations between different types of OMICs data for the same patient. This method is already successfully applied in personalized and translational medicine. Here, we propose to implement it at the level of the off-the-shelf product. Finally, we will develop the integrative database schema and interface of MetaMiner. The final deliverable for Phase I will be the functioning prototype. PUBLIC HEALTH RELEVANCE: We propose to develop a comprehensive, systems-level analytical system for integrative data analysis in cancer research. The platform, MetaMiner (Oncology), will include a comprehensive knowledge database on cancer biology and human biology in general, including protein interactions, gene-disease associations, pathways and networks, as well as cancer-relevant medicinal chemistry. MetaMiner will handle any type of cancer OMICs data and enable its comprehensive functional analysis. The system will include novel tools for clustering of cancer patients based on functional descriptors and integration of data of different types based on network topology.